Exposure to household biomass burning and coal-fired power plant emissions exacerbates biological aging in females- a case-control study

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Populations in the Indo-Gangetic Plain (IGP), one of the world’s most polluted and densely populated regions, experience disproportionate exposure to such emissions, compounded by meteorological and household factors. We investigated the environmental and biological impacts of emissions from the Farakka Thermal Power Plant in eastern India during winter 2021–2022. Particulate samples were collected at upwind and downwind sites and analyzed for metals and organic markers. Whole-blood RNA sequencing was performed from both sites to assess transcriptomic alterations and biological aging. Downwind populations were exposed to a higher chemical burden, with significantly elevated concentrations of V, Fe, Tl, and Se (p < 0.05), despite no significant difference in bulk PM₂.₅. Gene expression profiling revealed an enrichment of inflammatory and apoptotic pathways and downregulation of oxidative phosphorylation and reactive oxygen species signaling. Transcriptomic aging indicated a trend toward accelerated biological aging in the downwind population, with significantly greater aging in females at both sites (p = 0.047). Our integrative chemical–biological assessment demonstrates that coal-fired power plant emissions can induce systemic transcriptional reprogramming, inflammatory activation, and sex-linked biological aging. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental sciences/Environmental chemistry/Atmospheric chemistry Figures Figure 1 Figure 2 Figure 3 Introduction Coal-fired power plants are one of the primary sources of anthropogenic air pollution, significantly contributing to elevated levels of particulate matter (PM) and trace gases such as SO 2 , CO, NO X , and volatile organic compounds (VOCs). 1 Owing to their significant emissions, coal-fired power plants are frequently targeted in regulatory discussions because of their significant contributions to ambient particulate air pollution. 2 The health impacts of these emissions are particularly severe for populations living near coal-fired power plants, who face heightened risks of respiratory and other health issues linked to poor air quality. Seasonal meteorological conditions can further aggravate these impacts, especially during winter, when temperature inversions and stagnant air masses can worsen air quality. 3 In regions such as the Indo-Gangetic Plain (IGP), these challenges are compounded by dense population clusters, industrial activities, and widespread household use of solid fuels (e.g., wood, charcoal, animal dung), further complicating air quality. 4 The eastern IGP region, in particular, is highly vulnerable to the seasonal westerly outflow, which adds a significant burden of regionally transported aerosol to locally generated aerosol. 5 , 6 Emissions from the IGP have been estimated to account for nearly 46% of total premature mortality in India and approximately 8% in other regions via long-range transport of air pollutants. 7 According to Paulot et al., the IGP, home to over 800 million people, experiences extremely high PM 2.5 levels, driven mainly by coal-fired power plant emissions. These levels intensify during winter due to reduced near-surface wind speeds and limited atmospheric dispersion. 8 A growing body of evidence underscores the serious health risks associated with air pollution in the IGP. Studies show that existing and planned coal-fired power plants in India are linked to significant mortality impacts in the IGP. 9 Epidemiological studies provide causal evidence that residents of the IGP experience 49% higher exposure to PM 2.5 and a 2.6-year reduction in life expectancy compared to less polluted districts, underscoring the severe health burden of sustained particulate matter emissions in the region. 10 Consistent with this, studies have highlighted that submicron PM, predominantly found in densely populated regions such as the IGP, poses heightened risks of cardiovascular and respiratory morbidity due to its toxic chemical composition. 11 Beyond cardiopulmonary and respiratory effects, recent studies link air pollution exposure to systemic biological changes. Studies have shown that prolonged exposure accelerates biological aging, particularly affecting older populations 12 , and has notable impacts on cognitive skills. 13 Research from China similarly demonstrates that air pollution significantly increased the risk of chronic diseases, particularly affecting the health of middle-aged and older adults, with the most pronounced impacts observed among the elderly, women, urban residents, and individuals with lower incomes. 14 In the United States, occupational exposure in the trucking industry has been associated with alterations in gene expression related to oxidative stress and inflammation, suggesting a potential mechanism for pollution-induced biological aging. 15 Despite these advances, such integrative studies combining environmental chemistry with molecular biomarkers remain absent for the IGP. To address this critical knowledge gap, we investigated the chemical and biological footprint of emissions from a coal-fired power plant in eastern India (Farakka Thermal Power Plant; 24°46’N, 87°53’E) during the winter of 2021–2022. The study compared upwind (25°02′N, 88°08′E) and downwind (24°40′N, 87°55′E) communities to assess spatial differences in exposure. We conducted detailed chemical profiling of organic compounds (levoglucosan, n-alkanes, and polycyclic aromatic hydrocarbons) alongside critical metals including vanadium (V), manganese (Mn), iron (Fe), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), thallium (Tl), and lead (Pb), to characterize the spectrum of circulating chemicals in the environment. Many of these compounds, especially PAHs and transition metals, serve as combustion markers and are known to exacerbate respiratory complications. Complementing the chemical analyses, we employed whole-blood transcriptomics to explore systemic biological responses to chronic exposure.. By applying a transcriptomics aging clock 16 , we sought to identify molecular signatures associated with pollutant exposure and accelerated biological aging among individuals residing downwind of the power plant. Together, these integrated chemical and biological datasets offer new insights into the environmental and health impacts of coal-fired power plant emissions in one of the world’s most polluted and densely populated regions. Result Chemical footprint during winter indicates increased chemical burden downwind of the coal-fired power plants: We quantified a suite of metals, V, Mn, Fe, Zn, As, Se, Cd, Tl, and Pb, as well as source-specific organic compounds including levoglucosan, n -alkanes (C 21 –C 33 ), and polycyclic aromatic hydrocarbons (PAHs) such as benz[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IndP), dibenzo[a,h]pyrene (DBahA), and benzo[ghi]perylene (BghiP). We observed significantly elevated levels of V, Fe, Tl, Se, and specific n -alkanes (C 23 , C 25 , and C 32 ), alongside reduced concentrations of C 22 and C 30 n -alkanes at the downwind site (p < 0.05) (Fig. 1A). There was no significant difference in PM 2.5 between the sites (p = 0.386). Still, there was a trend toward higher levels on the downwind side, possibly due to both sites being heavily affected by pollutants transported from the middle and upper IGP during winter. As depicted in Fig. 1B, the concentration-weighted trajectories indicate that the major emission sources affecting the downwind site were located in the vicinity of the power plant and to the northwest. In contrast, air masses reaching the upwind site appeared to originate primarily from the Malda City area and were more spatially dispersed. These results confirm a distinct chemical fingerprint downwind of the coal-fired power plant, consistent with enhanced local and regional pollutant loading. Blood cell transcriptomics identified a unique pattern in the downwind. After excluding samples with low RIN values, we performed whole-blood transcriptomics on individuals residing upwind (n = 26) and downwind (n = 23) of the coal plant. The clinical and demographic data are presented in Table 1. The cohorts are well-matched in terms of age (p = 0.142) and gender (p = 0.71). Significant differences were observed in the type of cooking fuel (p = 0.007). Higher proportions of LPG were used by the residents of the upwind (31%) compared to the downwind (4%) side, while the use of both LPG and biomass was higher in the downwind (78%) compared to the upwind (46%). No significant difference in best FVC (Forced Vital Capacity) and FEF₂₅₋₇₅ (Forced Expiratory Flow between 25% and 75% of FVC) between the sides is observed. Table 1. Participants' demographic, socio-economic, and lung function data. Parameters Upwind Downwind P value Number 26 23 Age in years median (Q1-Q3) 45 (34.5-56) 40 (30-49.5) 0.142 Gender, female, n (%) 12 (46%) 16 (69.5) 0.71 Years of Education, median (Q1-Q3) 4 (3-9.5) 0 (0-7.25) 0.0198 Per capita monthly income, Median (Q1-Q3) 2500 (2083-2916) 2000 (1400-2666) 0.336 Below Level of Poverty (BLP), Yes, n (%) Missing, n (%) 10 (38%) 2 (7.6%) 17 (73%) 0 0.148 Cooking, Yes, n (%) Missing, n (%) 10 (38%) 4 (15%) 14 (61%) 1 (4%) 0.3429 Smoking, Yes, n (%) Missing, n(%) 1 (4%) 1 (4%) 2 (9%) 0 1 Married, Yes, n(%) 21 (81%) 23 (100%) 1 Cooking Fuel Type, n (%) LPG Biomass LPG and Biomass Missing 8 (31%) 1 (4%) 12 (46%) 5 (19%) 1 (4%) 6 (26%) 18 (78%) 0 0.007 Best FVC, Median (Q1-Q3) 2.43 (1.98-2.81) 2.44 (1.93-2.78) 0.75 Best FEF 25-75 Median (Q1-Q3) 2.12 (1.62-2.73) 2.195(1.73-2.69) 0.975 The differential gene expression identified 728 upregulated and 898 downregulated transcripts in residents living downwind (adjusted p < 0.05) (Fig. 2A). The principal component analysis revealed that the first two principal components (PC1: 68%, PC2: 8.5%) explained the highest proportion of variance (Fig. 2B), a finding also reflected in the hierarchical clustering, as shown in the heatmap (Fig. 2C). Directional-gene set enrichment analysis identified the upregulation of inflammatory response pathways, including TNF-α signaling via NF-κB and apoptosis processes, alongside downregulation of oxidative phosphorylation and reactive oxygen species (ROS) pathways (adjusted p < 0.05; Fig. 2D). This data indicates that individuals residing downwind of the power plant exhibit distinct gene expression changes compared to those upwind, reflecting inflammation and metabolic stress, consistent with chronic pollutant exposure. Females had accelerated biological aging irrespective of their residence location Given the significant changes in the gene expression profile, we aim to understand the impact on biological aging further, as measured by transcriptomic age. We used the RNAage calculator, which revealed a relatively strong correlation (R = 0.64, p = 0.001), with chronological age providing the best fit for our data (Fig. 3A). To account for the nonlinearity of the aging process, in which the rate of aging and the manifestation of age-related changes vary over time, we calculated delta age by subtracting chronological age from transcriptomic age. The results indicated a negative correlation between delta age and chronological age across both sites (R = -0.92 and − 0.93, p < 0.001) (Fig. 3B). The median (IQR) delta age on the downwind side was 7.62 (-0.377 to 15.4), and on the upwind side, it was 2.14 (-7.83 to 11.7); however, the difference was not statistically significant (Fig. 3C). This suggests that as individuals age, the deviation between their transcriptomic and chronological ages decreases, potentially reflecting age-dependent shifts in gene expression dynamics. We further checked whether the pattern has a sex bias. We identified that females were more affected by accelerated transcriptomic aging at both sites, with a significant increase in downwind compared to males (p = 0.047) and a borderline significance in upwind (p = 0.051). The sex-specific differential gene expression and pathway analysis between the upwind and downwind groups showed a similar pattern to that observed in the overall cohort (Fig. 3E). Collectively, these results suggest that chronic exposure to coal-related emissions may influence transcriptomic aging through inflammatory and metabolic pathways, with a more pronounced effect among females, particularly at the downwind site. Discussion In this study, we examined the chemical and biological footprint of emissions from a thermal power plant in eastern India during the winter of 2021–2022, focusing on comparative effects on populations residing upwind and downwind. Although ambient PM 2.5 levels did not differ significantly between the two sites, a consistent upward trend was evident downwind, reflecting both local emissions from the coal-fired power plant and the influence of long-range transport of pollutants from the IGP. 9 The compositional analysis of the particulate fraction revealed distinct source contributions, with elevated levels of metals (V, Fe, Tl, Se) and certain n -alkanes downwind, underscoring the chemical complexity and potential toxicity of the air mass affecting the downwind population. To assess the biological relevance of pollutant exposures, we conducted whole-blood transcriptomic profiling, which revealed a distinct molecular signature in individuals residing downwind of the power plant. The downwind group exhibited upregulation of genes involved in inflammation, stress response, and apoptosis, coupled with downregulation of oxidative phosphorylation and ROS-regulating pathways. This coordinated transcriptional response suggests an inflammatory and metabolic imbalance characteristic of chronic exposure to pollutants. Notably, women appeared more vulnerable to transcriptomic age acceleration, with significantly greater biological aging in females than in males at both sites. This may be linked to and driven by combined exposure from environmental pollution and household cooking practices and behaviors. To our knowledge, this is the first study to document differential molecular effects on populations downwind, as evidenced by whole-blood transcriptomic profiling that revealed unique molecular signatures in response to exposure. The observed gene-expression signatures are consistent with broader evidence linking air pollution to chronic inflammation, oxidative stress, and metabolic disruption, key pathways implicated in stroke, cerebrovascular injury, and neurodegenerative diseases. 17 – 19 The activation of pro-inflammatory and apoptotic signaling, alongside mitochondrial dysfunction and impaired oxidative metabolism, may contribute to energy imbalance, immune dysregulation, and enhanced susceptibility to cardiopulmonary or neurodegenerative outcomes.²¹–²³ Collectively, these molecular alterations indicate a systemic biological response to sustained environmental stressors, highlighting the health vulnerability of communities downwind of large emission sources. One of the profound findings of our study is a sex-linked pattern of accelerated transcriptomic aging. Women exhibited greater biological aging than men at both the upwind and downwind sites, likely due to the combined effects of ambient pollution and household cooking exposure. This suggests two things: first, there is likely to be a strong sex-specific bias in the health effects stemming from household air pollution since women and children spend the most time near domestic hearths in rural India (WHO, 2018), and given that ~ 43% of the Indian population is still dependent on solid fuels for cooking, heating and household energy services (HEI, 2018). Second, this finding also suggests that sex-specific susceptibility, possibly driven by differential inflammatory and metabolic responses, plays a critical role in modulating pollution-related health effects. Previous microarray data demonstrate that particulate matter exposure can lead to sex-specific changes in gene expression. 20 Our study extends these findings by demonstrating, for the first time, pollution-linked acceleration of biological aging in communities near coal-fired power plants. The differential biological aging underscores the need for focused research on sex-specific molecular pathways and their interactions with environmental factors to influence health outcomes. Understanding these mechanisms may provide valuable insights into how chronic inflammatory and metabolic stress, previously described in populations living with conditions such as HIV²⁵, could similarly drive environmentally induced aging processes. In light of these results, the disproportionate health burden faced by women due to overlapping ambient and household exposures warrants systematic investigation through longitudinal and multi-omics studies. Despite these critical findings, several limitations merit consideration. First, the sample size was modest, which constrained statistical interpretation. Second, the cross-sectional design limits causal inference and does not capture temporal dynamics or long-term health outcomes. Third, focusing on a single geographic region may limit the generalizability of the findings to broader populations exposed to coal-fired power plant emissions. Nevertheless, this work represents a proof-of-concept investigation that provides novel evidence linking environmental exposure to biological aging at the molecular level. Even within a limited cohort, we observed pronounced transcriptomic differences between populations residing upwind and downwind of the coal-fired power plant. In conclusion, our findings demonstrate that large point-source emissions can trigger distinct and potentially harmful systemic biological responses, even in regions with high background pollution levels. These results highlight the importance of localized environmental health assessments and emphasize the need to consider sex-specific vulnerabilities when developing targeted strategies to mitigate pollution-related health risks. By integrating chemical, molecular, and demographic dimensions, our work provides an essential step toward understanding how chronic environmental exposures intersect with biological aging and vulnerability, particularly in resource-limited and high-exposure settings such as the IGP. Method Chemical collection and characterization From December 2021 to November 2022, 106 and 121 PM 2.5 samples were collected at the upwind (25°02′N, 88°08′E) and downwind (24°40′N, 87°55′E) sites, respectively. A total of 17 and 18 samples from the upwind and downwind sites, respectively, were collected during the winter season (December 2021 to February 2022) and analyzed. Organic pollutants (levoglucosan, n-alkanes, and polycyclic aromatic hydrocarbons) were extracted with solvents and analyzed on gas chromatograph-mass spectrometry (GC-MS) and GC-MS/MS). 21 Elements (V, Mn, Fe, Zn, As, Se, Cd, Tl, and Pb) were extracted and analyzed with microwave-assisted acid digestion and inductively coupled plasma mass spectrometry (ICP-MS). Forty-eight-hour backward trajectories and weighted concentration-weighted trajectories (WCWT) were calculated using samples collected from Dec 2021 to Nov 2022, with the Global Data Assimilation System (GDAS, 1.0°) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The experimental and modeling details are available in our previous work. 21 Spirometry In the present work, spirometry was performed using an auto-calibrated PC-driven spirometer (Easy on-PC, Medical Technologies, USA) on subjects in a sitting posture as per American Thoracic Society (ATS)/European Respiratory Society (ERS) recommendations. Reproducibility of results, i.e., FEV1 and FVC, each varying by ≤ 150 mL between corresponding largest and next-largest values, was ensured when selecting acceptable tests. Blood sample collection : As part of the larger study, 22 100 households from each of the upwind and downwind sites were surveyed using a structured questionnaire. The households were selected using simple random sampling, and the samples represent the study sites (95% confidence interval with a < 5% margin of error). A total of 3 mL of fresh blood was collected from participants in Tempus™ Blood RNA tubes containing 6 mL of stabilizing reagent (Thermo Fisher Scientific, Cat. No: 4342792) from 29 and 31 willing adults in the upwind and downwind sites, respectively, during winter 2021- 22. All participants provided written informed consent and voluntarily agreed to contribute to the study. Whole Blood RNA sequencing : RNA isolation from the samples was performed using the Tempus™ Spin RNA Isolation Kit (Thermo Fisher Scientific, Cat. No: 4378926) according to the manufacturer’s protocol to extract total RNA from whole blood, and the RNA quality was assessed. Library preparation was performed using the NEBNext® Ultra™ II RNA Library Prep Kit for Illumina® (Cat. No. E7770, New England Biolabs) according to the manufacturer's instructions. The libraries were sequenced on the Illumina NovaSeq 6000 platform using a paired-end 150 bp (PE150) sequencing strategy. The analysis was performed in PHIXGEN Pvt. Ltd., New Delhi, India. Bioinformatics Analysis : The RNASeq data was processed using nf-core/rnaseq v3.16.0 (doi: 10.5281/zenodo.1400710 ) of the nf-core collection of workflows, 23 utilising reproducible software environments from the Bioconda 24 and Biocontainers 25 projects. The pipeline was executed with Nextflow v24.04.4. 26 Briefly, the nf-core/rnaseq pipeline used the cutadapt v3.4 (DOI: 10.14806/ej. 17.1.200) tool to remove adapter sequences, primers, poly-A tails, and other types of unwanted sequences. The pre-processed reads were then aligned to the human reference genome (GRCh38) using the tool STAR v5.1.0. 27 Salmon v1.10.1 was used for gene expression quantification. 28 Differential gene expression analysis was performed using the DESeq2 v1.44.0 R package. 29 R package RUVSeq v1.38.0 was also used for differential expression analysis to remove unwanted variation in the data. 30 Gene set analysis was performed using the R package piano v2.20.0 31 and hallmark gene-sets obtained from MSigDB. 32 Principal component analysis was performed using the R package PCAtools v2.16.0. Heatmaps are generated using the R package ComplexHeatmap v2.20.0 33 and R package ggpubr v0.6.0 was used for correlation analysis. R package RNAAgeCalc v 1.16.0 was used to compute transcriptional age, which performed well over the other RNA age estimators as described previously. 16 Declarations Ethical Clearance: This study was approved by the Calcutta Medical Research Institute (IEC/2022/ACD/Exp-APRV/01) for lung health assessment and blood sample collection, and the Indian Institute of Technology-Mandi (IITM/IEC(H)/2022/SDG/P9) for health and socioeconomic surveys. The study was approved by Etiksprövningsmyndigheten, Sweden (Dnr 202204882-01) for the handling of all data for research and interpretation. Written consent was obtained voluntarily before the collection of demographic and health data and blood sampling. Funding: The study is funded by the Swedish Research Council (Vetensakpsrådet Grant 2020-03605) to JR. UN acknowledges the funding received from Karolinska Institute Consolidator Grants (2-117/2023). Acknowledgement: We acknowledge the NOAA Air Resources Laboratory (ARL) and the READY website (www.ready.noaa.gov) for providing the HYSPLIT transport model. We are thankful to Kalachand High School, Old Malda, and Baharagachhi High School, Murshidabad, for their support towards aerosol collection and logistics. We acknowledge the assistance of the field investigators (Md. Taimur Bin Kashim Khan and his team) and the technician (Mr. Brojoballav Roy), who conducted the socioeconomic surveys and lung function tests, respectively. Conflict of Interest: None to declare. AI Use: The authors used AI-assisted editing tools (ChatGPT-4) exclusively for language refinement, including grammar correction and sentence structure improvement. References Guttikunda SK, Jawahar PJAE. Atmospheric emissions and pollution from the coal-fired thermal power plants in India. 2014; 92: 449–60. Kim C, Henneman LR, Choirat C, Zigler CM. Health effects of power plant emissions through ambient air quality. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9003238","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600861254,"identity":"9732ce28-0553-4a50-b659-e97b9c7e5916","order_by":0,"name":"Anoop Ambikan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anoop","middleName":"","lastName":"Ambikan","suffix":""},{"id":600861255,"identity":"12339728-a959-4b59-b4fa-2a4a4dabfba1","order_by":1,"name":"Shyamasree Dasgupta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shyamasree","middleName":"","lastName":"Dasgupta","suffix":""},{"id":600861256,"identity":"a3dc82de-cecb-4a8b-8519-3e0392cd51e3","order_by":2,"name":"Raja Dhar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"","lastName":"Dhar","suffix":""},{"id":600861257,"identity":"4408be3d-9696-417b-8b63-f081953c6c82","order_by":3,"name":"Vikas Sood","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vikas","middleName":"","lastName":"Sood","suffix":""},{"id":600861258,"identity":"f6c14d07-bc23-4ea0-a08b-80c906cdf5ef","order_by":4,"name":"Chen Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Luo","suffix":""},{"id":600861259,"identity":"ccb97218-efbf-46c4-a3a7-2c869a9aa444","order_by":5,"name":"Sayantan Sarkar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sayantan","middleName":"","lastName":"Sarkar","suffix":""},{"id":600861260,"identity":"b86c1503-f187-47c9-925e-1eb0c3f6be25","order_by":6,"name":"Megha Anand","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Megha","middleName":"","lastName":"Anand","suffix":""},{"id":600861261,"identity":"37d03b06-513d-46d0-a344-a46a669984d1","order_by":7,"name":"Yogita Rawat","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yogita","middleName":"","lastName":"Rawat","suffix":""},{"id":600861262,"identity":"ff503e70-2a45-46bf-801d-0362a4367d67","order_by":8,"name":"Apostolos Bossios","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Apostolos","middleName":"","lastName":"Bossios","suffix":""},{"id":600861263,"identity":"f182c4e7-6802-4da9-abdf-5b9b57995ddd","order_by":9,"name":"Joyanto Routh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Joyanto","middleName":"","lastName":"Routh","suffix":""},{"id":600861253,"identity":"93e71696-73ff-4732-a82b-67f23589c293","order_by":10,"name":"Ujjwal Neogi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYFCCAwkMDAVQ5ocDEsRqMYAwD84gTgsIQLUw8xwgRvHBA88kfhgw5PGzt188bHPGgkG+vYGAlgMH0iR7DBiKJXvOFBzOuSHBYHCGgFUgLTd4DBgSN9zISTic8wGoRSKBsJabf4Ba9oO0WAC1yM9/QFjLbbAtEukHDjMAHcZwA78OBskDB9J/yxhIJM44c4bhYM8ZCR6DMwQcxnfjTLLhmwqbxP729scffhyrk5NvP0DAGgmwoaAY5AHHDg8B9UDADzeU/QFh1aNgFIyCUTAiAQC970627MoqMAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0844-3338","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Ujjwal","middleName":"","lastName":"Neogi","suffix":""}],"badges":[],"createdAt":"2026-03-01 18:05:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104373970,"identity":"6dfceadf-4177-42a7-973d-657d9ed8f26f","added_by":"auto","created_at":"2026-03-11 06:04:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4559231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation in metal and organic pollutant concentrations and source contributions between upwind and downwind sites.\u003c/strong\u003e\u003cbr\u003e\n(A) Significantly elevated levels of V, Fe, Tl, Se, and \u003cem\u003en\u003c/em\u003e-alkanes (C\u003csub\u003e23\u003c/sub\u003e, C\u003csub\u003e25\u003c/sub\u003e, C\u003csub\u003e32\u003c/sub\u003e) were observed at the downwind site, along with reduced concentrations of C\u003csub\u003e22\u003c/sub\u003e and C\u003csub\u003e30\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). PM₂.₅ concentrations showed no significant difference between sites (\u003cem\u003ep\u003c/em\u003e = 0.386) but displayed a higher trend downwind.\u003cbr\u003e\n(B) Concentration-weighted trajectory (CWT) analysis showing dominant source regions influencing each site.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9003238/v1/e4cb3e83027f3f44689d60bd.png"},{"id":104373973,"identity":"e5ba5c5d-4ba0-4b98-8b50-30ecf5020f74","added_by":"auto","created_at":"2026-03-11 06:04:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6365226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Volcano plot showing differential gene expression analysis results between the downwind and upwind groups. Adjusted p-value and Log2 Fold Change corresponding to gene expression change between two groups were obtained from DESeq2. Non-significant differential expressions (p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05) are represented by grey color. B) Visualization of sample distribution using the expression data of significantly regulated genes between the two groups. Principal Component Analysis (PCA) was used to evaluate the sample distribution. C) The expression landscape of significantly regulated (p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05) genes between the two groups is visualized as a heatmap. Column annotation represents sample groups, and row annotation represents the direction of gene regulation in downwind compared to upwind. D) Bubble plot showing gene set analysis results between the two groups. Gene set statistics for the distinct-directional up test and distinct-directional down test are represented on the y-axis, and statistical significance with respect to adjusted p-value is plotted on the x-axis. Gene sets denoted by red color bubbles are up-regulated, and those denoted by green color bubbles are down-regulated in down-wind compared to up-wind.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9003238/v1/0bc99bebfadc40992cee0cab.png"},{"id":104373972,"identity":"084125c7-d558-445e-a664-717aac020053","added_by":"auto","created_at":"2026-03-11 06:04:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2722306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Correlation between predicted RNA-age and chronological age in the upwind group. The linear regression line, Spearman's rho, and p-value computed are indicated in the figure. B) Correlation between delta-age and chronological age in the upwind and downwind groups. The linear regression line, Spearman's rho, and p-value computed are indicated in the figure. C) Boxplot showing differences in delta-age between upwind and downwind sample groups. P-value obtained from the Mann-Whitney U test is displayed. D) Boxplot showing differences in delta-age between males and females in upwind and downwind sample groups. P-values obtained from the Mann-Whitney U test are indicated. E) Venn diagram showing the gene set enrichment analysis in a sex-specific way and combined analysis. Key common pathways are shown.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9003238/v1/026f40a15e7264c22d563873.png"},{"id":104779844,"identity":"b016da97-fc8b-4fa4-8fc7-c7bc887b4933","added_by":"auto","created_at":"2026-03-17 07:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13539566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9003238/v1/67c766fe-da09-43c4-94f0-7c16304497ef.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Exposure to household biomass burning and coal-fired power plant emissions exacerbates biological aging in females- a case-control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoal-fired power plants are one of the primary sources of anthropogenic air pollution, significantly contributing to elevated levels of particulate matter (PM) and trace gases such as SO\u003csub\u003e2\u003c/sub\u003e, CO, NO\u003csub\u003eX\u003c/sub\u003e, and volatile organic compounds (VOCs).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Owing to their significant emissions, coal-fired power plants are frequently targeted in regulatory discussions because of their significant contributions to ambient particulate air pollution.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The health impacts of these emissions are particularly severe for populations living near coal-fired power plants, who face heightened risks of respiratory and other health issues linked to poor air quality. Seasonal meteorological conditions can further aggravate these impacts, especially during winter, when temperature inversions and stagnant air masses can worsen air quality.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn regions such as the Indo-Gangetic Plain (IGP), these challenges are compounded by dense population clusters, industrial activities, and widespread household use of solid fuels (e.g., wood, charcoal, animal dung), further complicating air quality.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The eastern IGP region, in particular, is highly vulnerable to the seasonal westerly outflow, which adds a significant burden of regionally transported aerosol to locally generated aerosol.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Emissions from the IGP have been estimated to account for nearly 46% of total premature mortality in India and approximately 8% in other regions via long-range transport of air pollutants.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e According to Paulot et al., the IGP, home to over 800\u0026nbsp;million people, experiences extremely high PM\u003csub\u003e2.5\u003c/sub\u003e levels, driven mainly by coal-fired power plant emissions. These levels intensify during winter due to reduced near-surface wind speeds and limited atmospheric dispersion.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA growing body of evidence underscores the serious health risks associated with air pollution in the IGP. Studies show that existing and planned coal-fired power plants in India are linked to significant mortality impacts in the IGP.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Epidemiological studies provide causal evidence that residents of the IGP experience 49% higher exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and a 2.6-year reduction in life expectancy compared to less polluted districts, underscoring the severe health burden of sustained particulate matter emissions in the region.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Consistent with this, studies have highlighted that submicron PM, predominantly found in densely populated regions such as the IGP, poses heightened risks of cardiovascular and respiratory morbidity due to its toxic chemical composition.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBeyond cardiopulmonary and respiratory effects, recent studies link air pollution exposure to systemic biological changes. Studies have shown that prolonged exposure accelerates biological aging, particularly affecting older populations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and has notable impacts on cognitive skills.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Research from China similarly demonstrates that air pollution significantly increased the risk of chronic diseases, particularly affecting the health of middle-aged and older adults, with the most pronounced impacts observed among the elderly, women, urban residents, and individuals with lower incomes.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In the United States, occupational exposure in the trucking industry has been associated with alterations in gene expression related to oxidative stress and inflammation, suggesting a potential mechanism for pollution-induced biological aging.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Despite these advances, such integrative studies combining environmental chemistry with molecular biomarkers remain absent for the IGP.\u003c/p\u003e \u003cp\u003eTo address this critical knowledge gap, we investigated the chemical and biological footprint of emissions from a coal-fired power plant in eastern India (Farakka Thermal Power Plant; 24\u0026deg;46\u0026rsquo;N, 87\u0026deg;53\u0026rsquo;E) during the winter of 2021\u0026ndash;2022. The study compared upwind (25\u0026deg;02\u0026prime;N, 88\u0026deg;08\u0026prime;E) and downwind (24\u0026deg;40\u0026prime;N, 87\u0026deg;55\u0026prime;E) communities to assess spatial differences in exposure. We conducted detailed chemical profiling of organic compounds (levoglucosan, n-alkanes, and polycyclic aromatic hydrocarbons) alongside critical metals including vanadium (V), manganese (Mn), iron (Fe), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), thallium (Tl), and lead (Pb), to characterize the spectrum of circulating chemicals in the environment. Many of these compounds, especially PAHs and transition metals, serve as combustion markers and are known to exacerbate respiratory complications. Complementing the chemical analyses, we employed whole-blood transcriptomics to explore systemic biological responses to chronic exposure.. By applying a transcriptomics aging clock\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, we sought to identify molecular signatures associated with pollutant exposure and accelerated biological aging among individuals residing downwind of the power plant. Together, these integrated chemical and biological datasets offer new insights into the environmental and health impacts of coal-fired power plant emissions in one of the world\u0026rsquo;s most polluted and densely populated regions.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eChemical footprint during winter indicates increased chemical burden downwind of the coal-fired power plants:\u003c/h2\u003e\n \u003cp\u003eWe quantified a suite of metals, V, Mn, Fe, Zn, As, Se, Cd, Tl, and Pb, as well as source-specific organic compounds including levoglucosan, \u003cem\u003en\u003c/em\u003e-alkanes (C\u003csub\u003e21\u003c/sub\u003e\u0026ndash;C\u003csub\u003e33\u003c/sub\u003e), and polycyclic aromatic hydrocarbons (PAHs) such as benz[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IndP), dibenzo[a,h]pyrene (DBahA), and benzo[ghi]perylene (BghiP). We observed significantly elevated levels of V, Fe, Tl, Se, and specific \u003cem\u003en\u003c/em\u003e-alkanes (C\u003csub\u003e23\u003c/sub\u003e, C\u003csub\u003e25\u003c/sub\u003e, and C\u003csub\u003e32\u003c/sub\u003e), alongside reduced concentrations of C\u003csub\u003e22\u003c/sub\u003e and C\u003csub\u003e30\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkanes at the downwind site (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1A). There was no significant difference in PM\u003csub\u003e2.5\u003c/sub\u003e between the sites (p\u0026thinsp;=\u0026thinsp;0.386). Still, there was a trend toward higher levels on the downwind side, possibly due to both sites being heavily affected by pollutants transported from the middle and upper IGP during winter. As depicted in Fig.\u0026nbsp;1B, the concentration-weighted trajectories indicate that the major emission sources affecting the downwind site were located in the vicinity of the power plant and to the northwest. In contrast, air masses reaching the upwind site appeared to originate primarily from the Malda City area and were more spatially dispersed. These results confirm a distinct chemical fingerprint downwind of the coal-fired power plant, consistent with enhanced local and regional pollutant loading.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBlood cell transcriptomics identified a unique pattern in the downwind.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAfter excluding samples with low RIN values, we performed whole-blood transcriptomics on individuals residing upwind (n\u0026thinsp;=\u0026thinsp;26) and downwind (n\u0026thinsp;=\u0026thinsp;23) of the coal plant. The clinical and demographic data are presented in Table\u0026nbsp;1. The cohorts are well-matched in terms of age (p\u0026thinsp;=\u0026thinsp;0.142) and gender (p\u0026thinsp;=\u0026thinsp;0.71). Significant differences were observed in the type of cooking fuel (p\u0026thinsp;=\u0026thinsp;0.007). Higher proportions of LPG were used by the residents of the upwind (31%) compared to the downwind (4%) side, while the use of both LPG and biomass was higher in the downwind (78%) compared to the upwind (46%). No significant difference in best FVC (Forced Vital Capacity) and FEF₂₅₋₇₅ (Forced Expiratory Flow between 25% and 75% of FVC) between the sides is observed.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Participants\u0026apos; demographic, socio-economic, and lung function data.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpwind\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownwind\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eAge in years median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e45 (34.5-56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e40 (30-49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eGender, female, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e12 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e16 (69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eYears of Education, median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e4 (3-9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e0 (0-7.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003ePer capita monthly income, Median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e2500 (2083-2916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e2000 (1400-2666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eBelow Level of Poverty (BLP), Yes, n (%)\u003c/p\u003e\n \u003cp\u003eMissing, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e10 (38%)\u003c/p\u003e\n \u003cp\u003e2 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e17 (73%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eCooking, Yes, n (%)\u003c/p\u003e\n \u003cp\u003eMissing, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e10 (38%)\u003c/p\u003e\n \u003cp\u003e4 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e14 (61%)\u003c/p\u003e\n \u003cp\u003e1 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.3429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eSmoking, Yes, n (%)\u003c/p\u003e\n \u003cp\u003eMissing, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e1 (4%)\u003c/p\u003e\n \u003cp\u003e1 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e2 (9%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eMarried, Yes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e21 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e23 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eCooking Fuel Type, n (%)\u003c/p\u003e\n \u003cp\u003eLPG\u003c/p\u003e\n \u003cp\u003eBiomass\u003c/p\u003e\n \u003cp\u003eLPG and Biomass\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (31%)\u003c/p\u003e\n \u003cp\u003e1 (4%)\u003c/p\u003e\n \u003cp\u003e12 (46%)\u003c/p\u003e\n \u003cp\u003e5 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (4%)\u003c/p\u003e\n \u003cp\u003e6 (26%)\u003c/p\u003e\n \u003cp\u003e18 (78%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eBest FVC, Median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e2.43 (1.98-2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e2.44 (1.93-2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.1538%;\"\u003e\n \u003cp\u003eBest FEF\u003csub\u003e25-75\u003c/sub\u003e Median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6399%;\"\u003e\n \u003cp\u003e2.12 (1.62-2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6039%;\"\u003e\n \u003cp\u003e2.195(1.73-2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6023%;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThe differential gene expression identified 728 upregulated and 898 downregulated transcripts in residents living downwind (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. 2A). The principal component analysis revealed that the first two principal components (PC1: 68%, PC2: 8.5%) explained the highest proportion of variance (Fig. 2B), a finding also reflected in the hierarchical clustering, as shown in the heatmap (Fig. 2C). Directional-gene set enrichment analysis identified the upregulation of inflammatory response pathways, including TNF-\u0026alpha; signaling via NF-\u0026kappa;B and apoptosis processes, alongside downregulation of oxidative phosphorylation and reactive oxygen species (ROS) pathways (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;2D). This data indicates that individuals residing downwind of the power plant exhibit distinct gene expression changes compared to those upwind, reflecting inflammation and metabolic stress, consistent with chronic pollutant exposure.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eFemales had accelerated biological aging irrespective of their residence location\u003c/h3\u003e\n\u003cp\u003eGiven the significant changes in the gene expression profile, we aim to understand the impact on biological aging further, as measured by transcriptomic age. We used the RNAage calculator, which revealed a relatively strong correlation (R\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;=\u0026thinsp;0.001), with chronological age providing the best fit for our data (Fig.\u0026nbsp;3A). To account for the nonlinearity of the aging process, in which the rate of aging and the manifestation of age-related changes vary over time, we calculated delta age by subtracting chronological age from transcriptomic age. The results indicated a negative correlation between delta age and chronological age across both sites (R = -0.92 and \u0026minus;\u0026thinsp;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;3B). The median (IQR) delta age on the downwind side was 7.62 (-0.377 to 15.4), and on the upwind side, it was 2.14 (-7.83 to 11.7); however, the difference was not statistically significant (Fig.\u0026nbsp;3C). This suggests that as individuals age, the deviation between their transcriptomic and chronological ages decreases, potentially reflecting age-dependent shifts in gene expression dynamics. We further checked whether the pattern has a sex bias. We identified that females were more affected by accelerated transcriptomic aging at both sites, with a significant increase in downwind compared to males (p\u0026thinsp;=\u0026thinsp;0.047) and a borderline significance in upwind (p\u0026thinsp;=\u0026thinsp;0.051). The sex-specific differential gene expression and pathway analysis between the upwind and downwind groups showed a similar pattern to that observed in the overall cohort (Fig.\u0026nbsp;3E). Collectively, these results suggest that chronic exposure to coal-related emissions may influence transcriptomic aging through inflammatory and metabolic pathways, with a more pronounced effect among females, particularly at the downwind site.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined the chemical and biological footprint of emissions from a thermal power plant in eastern India during the winter of 2021\u0026ndash;2022, focusing on comparative effects on populations residing upwind and downwind. Although ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels did not differ significantly between the two sites, a consistent upward trend was evident downwind, reflecting both local emissions from the coal-fired power plant and the influence of long-range transport of pollutants from the IGP.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The compositional analysis of the particulate fraction revealed distinct source contributions, with elevated levels of metals (V, Fe, Tl, Se) and certain \u003cem\u003en\u003c/em\u003e-alkanes downwind, underscoring the chemical complexity and potential toxicity of the air mass affecting the downwind population.\u003c/p\u003e \u003cp\u003eTo assess the biological relevance of pollutant exposures, we conducted whole-blood transcriptomic profiling, which revealed a distinct molecular signature in individuals residing downwind of the power plant. The downwind group exhibited upregulation of genes involved in inflammation, stress response, and apoptosis, coupled with downregulation of oxidative phosphorylation and ROS-regulating pathways. This coordinated transcriptional response suggests an inflammatory and metabolic imbalance characteristic of chronic exposure to pollutants. Notably, women appeared more vulnerable to transcriptomic age acceleration, with significantly greater biological aging in females than in males at both sites. This may be linked to and driven by combined exposure from environmental pollution and household cooking practices and behaviors.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to document differential molecular effects on populations downwind, as evidenced by whole-blood transcriptomic profiling that revealed unique molecular signatures in response to exposure. The observed gene-expression signatures are consistent with broader evidence linking air pollution to chronic inflammation, oxidative stress, and metabolic disruption, key pathways implicated in stroke, cerebrovascular injury, and neurodegenerative diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The activation of pro-inflammatory and apoptotic signaling, alongside mitochondrial dysfunction and impaired oxidative metabolism, may contribute to energy imbalance, immune dysregulation, and enhanced susceptibility to cardiopulmonary or neurodegenerative outcomes.\u0026sup2;\u0026sup1;\u0026ndash;\u0026sup2;\u0026sup3; Collectively, these molecular alterations indicate a systemic biological response to sustained environmental stressors, highlighting the health vulnerability of communities downwind of large emission sources.\u003c/p\u003e \u003cp\u003eOne of the profound findings of our study is a sex-linked pattern of accelerated transcriptomic aging. Women exhibited greater biological aging than men at both the upwind and downwind sites, likely due to the combined effects of ambient pollution and household cooking exposure. This suggests two things: first, there is likely to be a strong sex-specific bias in the health effects stemming from household air pollution since women and children spend the most time near domestic hearths in rural India (WHO, 2018), and given that ~\u0026thinsp;43% of the Indian population is still dependent on solid fuels for cooking, heating and household energy services (HEI, 2018). Second, this finding also suggests that sex-specific susceptibility, possibly driven by differential inflammatory and metabolic responses, plays a critical role in modulating pollution-related health effects. Previous microarray data demonstrate that particulate matter exposure can lead to sex-specific changes in gene expression.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Our study extends these findings by demonstrating, for the first time, pollution-linked acceleration of biological aging in communities near coal-fired power plants. The differential biological aging underscores the need for focused research on sex-specific molecular pathways and their interactions with environmental factors to influence health outcomes. Understanding these mechanisms may provide valuable insights into how chronic inflammatory and metabolic stress, previously described in populations living with conditions such as HIV\u0026sup2;⁵, could similarly drive environmentally induced aging processes. In light of these results, the disproportionate health burden faced by women due to overlapping ambient and household exposures warrants systematic investigation through longitudinal and multi-omics studies.\u003c/p\u003e \u003cp\u003eDespite these critical findings, several limitations merit consideration. First, the sample size was modest, which constrained statistical interpretation. Second, the cross-sectional design limits causal inference and does not capture temporal dynamics or long-term health outcomes. Third, focusing on a single geographic region may limit the generalizability of the findings to broader populations exposed to coal-fired power plant emissions. Nevertheless, this work represents a proof-of-concept investigation that provides novel evidence linking environmental exposure to biological aging at the molecular level. Even within a limited cohort, we observed pronounced transcriptomic differences between populations residing upwind and downwind of the coal-fired power plant.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings demonstrate that large point-source emissions can trigger distinct and potentially harmful systemic biological responses, even in regions with high background pollution levels. These results highlight the importance of localized environmental health assessments and emphasize the need to consider sex-specific vulnerabilities when developing targeted strategies to mitigate pollution-related health risks. By integrating chemical, molecular, and demographic dimensions, our work provides an essential step toward understanding how chronic environmental exposures intersect with biological aging and vulnerability, particularly in resource-limited and high-exposure settings such as the IGP.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e \u003cstrong\u003eChemical collection and characterization\u003c/strong\u003e \u003cp\u003eFrom December 2021 to November 2022, 106 and 121 PM\u003csub\u003e2.5\u003c/sub\u003e samples were collected at the upwind (25\u0026deg;02\u0026prime;N, 88\u0026deg;08\u0026prime;E) and downwind (24\u0026deg;40\u0026prime;N, 87\u0026deg;55\u0026prime;E) sites, respectively. A total of 17 and 18 samples from the upwind and downwind sites, respectively, were collected during the winter season (December 2021 to February 2022) and analyzed. Organic pollutants (levoglucosan, n-alkanes, and polycyclic aromatic hydrocarbons) were extracted with solvents and analyzed on gas chromatograph-mass spectrometry (GC-MS) and GC-MS/MS).\u003csup\u003e21\u003c/sup\u003e Elements (V, Mn, Fe, Zn, As, Se, Cd, Tl, and Pb) were extracted and analyzed with microwave-assisted acid digestion and inductively coupled plasma mass spectrometry (ICP-MS). Forty-eight-hour backward trajectories and weighted concentration-weighted trajectories (WCWT) were calculated using samples collected from Dec 2021 to Nov 2022, with the Global Data Assimilation System (GDAS, 1.0\u0026deg;) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The experimental and modeling details are available in our previous work.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSpirometry\u003c/strong\u003e \u003cp\u003eIn the present work, spirometry was performed using an auto-calibrated PC-driven spirometer (Easy on-PC, Medical Technologies, USA) on subjects in a sitting posture as per American Thoracic Society (ATS)/European Respiratory Society (ERS) recommendations. Reproducibility of results, i.e., FEV1 and FVC, each varying by \u0026le;\u0026thinsp;150 mL between corresponding largest and next-largest values, was ensured when selecting acceptable tests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eBlood sample collection\u003c/b\u003e: As part of the larger study,\u003csup\u003e22\u003c/sup\u003e 100 households from each of the upwind and downwind sites were surveyed using a structured questionnaire. The households were selected using simple random sampling, and the samples represent the study sites (95% confidence interval with a\u0026thinsp;\u0026lt;\u0026thinsp;5% margin of error). A total of 3 mL of fresh blood was collected from participants in Tempus\u0026trade; Blood RNA tubes containing 6 mL of stabilizing reagent (Thermo Fisher Scientific, Cat. No: 4342792) from 29 and 31 willing adults in the upwind and downwind sites, respectively, during winter 2021- 22. All participants provided written informed consent and voluntarily agreed to contribute to the study.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWhole Blood RNA sequencing\u003c/b\u003e: RNA isolation from the samples was performed using the Tempus\u0026trade; Spin RNA Isolation Kit (Thermo Fisher Scientific, Cat. No: 4378926) according to the manufacturer\u0026rsquo;s protocol to extract total RNA from whole blood, and the RNA quality was assessed. Library preparation was performed using the NEBNext\u0026reg; Ultra\u0026trade; II RNA Library Prep Kit for Illumina\u0026reg; (Cat. No. E7770, New England Biolabs) according to the manufacturer's instructions. The libraries were sequenced on the Illumina NovaSeq 6000 platform using a paired-end 150 bp (PE150) sequencing strategy. The analysis was performed in PHIXGEN Pvt. Ltd., New Delhi, India.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioinformatics Analysis\u003c/b\u003e: The RNASeq data was processed using nf-core/rnaseq v3.16.0 (doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.1400710\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.1400710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) of the nf-core collection of workflows,\u003csup\u003e23\u003c/sup\u003e utilising reproducible software environments from the Bioconda\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and Biocontainers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e projects. The pipeline was executed with Nextflow v24.04.4.\u003csup\u003e26\u003c/sup\u003e Briefly, the nf-core/rnaseq pipeline used the cutadapt v3.4 (DOI: 10.14806/ej. 17.1.200) tool to remove adapter sequences, primers, poly-A tails, and other types of unwanted sequences. The pre-processed reads were then aligned to the human reference genome (GRCh38) using the tool STAR v5.1.0.\u003csup\u003e27\u003c/sup\u003e Salmon v1.10.1 was used for gene expression quantification.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Differential gene expression analysis was performed using the DESeq2 v1.44.0 R package.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e R package RUVSeq v1.38.0 was also used for differential expression analysis to remove unwanted variation in the data.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Gene set analysis was performed using the R package piano v2.20.0\u003csup\u003e31\u003c/sup\u003e and hallmark gene-sets obtained from MSigDB.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Principal component analysis was performed using the R package PCAtools v2.16.0. Heatmaps are generated using the R package ComplexHeatmap v2.20.0\u003csup\u003e33\u003c/sup\u003e and R package ggpubr v0.6.0 was used for correlation analysis. R package RNAAgeCalc v 1.16.0 was used to compute transcriptional age, which performed well over the other RNA age estimators as described previously.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Clearance:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Calcutta Medical Research Institute (IEC/2022/ACD/Exp-APRV/01) for lung health assessment and blood sample collection, and the Indian Institute of Technology-Mandi (IITM/IEC(H)/2022/SDG/P9) for health and socioeconomic surveys. The study was approved by Etikspr\u0026ouml;vningsmyndigheten, Sweden (Dnr 202204882-01) for the handling of all data for research and interpretation. Written consent was obtained voluntarily before the collection of demographic and health data and blood sampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe study is funded by the Swedish Research Council (Vetensakpsr\u0026aring;det Grant 2020-03605) to JR. UN acknowledges the funding received from Karolinska Institute Consolidator Grants (2-117/2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the NOAA Air Resources Laboratory (ARL) and the READY website (www.ready.noaa.gov) for providing the HYSPLIT transport model. We are thankful to Kalachand High School, Old Malda, and Baharagachhi High School, Murshidabad, for their support towards aerosol collection and logistics. We acknowledge the assistance of the field investigators (Md. Taimur Bin Kashim Khan and his team) and the technician (Mr. Brojoballav Roy), who conducted the socioeconomic surveys and lung function tests, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Use:\u0026nbsp;\u003c/strong\u003eThe authors used AI-assisted editing tools (ChatGPT-4) exclusively for language refinement, including grammar correction and sentence structure improvement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuttikunda SK, Jawahar PJAE. Atmospheric emissions and pollution from the coal-fired thermal power plants in India. 2014; 92: 449\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim C, Henneman LR, Choirat C, Zigler CM. 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Complex heatmaps reveal patterns and correlations in multidimensional genomic data. \u003cem\u003eBioinformatics\u003c/em\u003e 2016; 32(18): 2847\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9003238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoal-fired power plants are major contributors to anthropogenic air pollution, emitting fine particulate matter (PM) and toxic trace elements linked to adverse health outcomes. Populations in the Indo-Gangetic Plain (IGP), one of the world\u0026rsquo;s most polluted and densely populated regions, experience disproportionate exposure to such emissions, compounded by meteorological and household factors. We investigated the environmental and biological impacts of emissions from the Farakka Thermal Power Plant in eastern India during winter 2021\u0026ndash;2022. Particulate samples were collected at upwind and downwind sites and analyzed for metals and organic markers. Whole-blood RNA sequencing was performed from both sites to assess transcriptomic alterations and biological aging. Downwind populations were exposed to a higher chemical burden, with significantly elevated concentrations of V, Fe, Tl, and Se (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), despite no significant difference in bulk PM₂.₅. Gene expression profiling revealed an enrichment of inflammatory and apoptotic pathways and downregulation of oxidative phosphorylation and reactive oxygen species signaling. Transcriptomic aging indicated a trend toward accelerated biological aging in the downwind population, with significantly greater aging in females at both sites (p\u0026thinsp;=\u0026thinsp;0.047). Our integrative chemical\u0026ndash;biological assessment demonstrates that coal-fired power plant emissions can induce systemic transcriptional reprogramming, inflammatory activation, and sex-linked biological aging.\u003c/p\u003e","manuscriptTitle":"Exposure to household biomass burning and coal-fired power plant emissions exacerbates biological aging in females- a case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 06:04:10","doi":"10.21203/rs.3.rs-9003238/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4ab5af9b-97a6-4814-8234-b25e60867b0b","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63944555,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":63944556,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry/Atmospheric chemistry"}],"tags":[],"updatedAt":"2026-03-30T21:00:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 06:04:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003238","identity":"rs-9003238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}